Lesson 1
Welcome to Machine Learning
Meet with Sebastian and Katie to discuss machine learning.
Free
Intro to Machine Learning
Course
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set.
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set.
Intermediate
Last Updated March 7, 2022
No experience required
Course Lessons
Lesson 2
Naive Bayes
Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.
Lesson 3
SVM
Build an intuition about how support vector machines (SVMs) work and implement one using scikit-learn.
Lesson 4
Decision Trees
Learn about how the decision tree algorithm works, including the concepts of entropy and information gain.
Lesson 5
Choose Your Own Algorithm
In this mini project, you will extend your toolbox of algorithms by choosing your own algorithm to classify terrain data, including k-nearest neighbors, AdaBoost, and random forests.
Lesson 6
Datasets and Questions
Find out about the Enron data set used in the next lessons and mini-projects.
Lesson 7
Regressions
See how we can model continuous data using linear regression.
Lesson 8
Outliers
Sebastian discusses outlier detection and removal.
Lesson 9
Clustering
Learn about what unsupervised learning is and find out how to use scikit-learn's k-means algorithm.
Lesson 10
Feature Scaling
Learn about feature rescaling and find out which algorithms require feature rescaling before use.
Lesson 11
Feature Selection
Katie discusses when and why to use feature selection, and provides some methods for doing this.
Lesson 12
Text Learning
Find out how to use text data in your machine learning algorithm.
Lesson 13
PCA
Learn about data dimensionality and reducing the number of dimensions with principal component analysis (PCA).
Lesson 14
Validation
Learn more about testing, training, cross validation, and parameter grid searches in this lesson.
Lesson 15
Evaluation Metrics
How do we know if our classifier is performing well? Katie discusses different evaluation metrics for classifiers in this lesson.
Lesson 16
Tying It All Together
Spend some time reflecting on the course material with Sebastian and Katie!
Lesson 17
Final Project
Taught By The Best
Katie Malone
Instructor
Sebastian Thrun
Founder and Executive Chairman, Udacity
As the Founder and Chairman of Udacity, Sebastian's mission is to democratize education by providing lifelong learning to millions of students worldwide. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.
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Intro to Machine Learning
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